TrajectoryAnalysis: Trajectory analysis

In QinglinMei/RCSL: Rank Constrained Similarity Learning for single cell RNA sequencing data

Trajectory analysis

Description

Trajectory analysis

Usage

TrajectoryAnalysis(
  gfData,
  drData,
  S,
  clustRes,
  fontSize = 12,
  TrueLabel,
  startPoint,
  dataName = "",
  sim = TRUE,
  simMeasure = "kendall",
  VisualMethod = "umap"
)

Arguments

gfData	preprocessed gene expression data (each column represent a cell)
drData	preprocessed gene expression data (each column represent a cell)
S	the similarity matrix calculated by SimS() function
clustRes	the clustering results identified by RCSL
fontSize	the size of font in the plot
TrueLabel	the real cell types used to indicate the vertical axis
startPoint	the posiition of the starting cell in the matrix
dataName	the name of the data that will be showed in the plot
sim	indicate the input data is simialrity matrix or not
simMeasure	the calculation method of measuring the cluster centers' similarity
VisualMethod	the display method of 2-D visualization

Value

PseudoTimePlot, MSTPlot, TrajectoryPlot

Examples

gfData <- GenesFilter(yan)
TrueLabel <- ann$cell_type1
res_SimS <- SimS(gfData)
C <- EstClusters(res_SimS$drData,res_SimS$S)
res_BDSM <- BDSM(res_SimS$S,C)
TrajectoryAnalysis(gfData,res_SimS$drData,res_SimS$S,res_BDSM$y,
                   TrueLabel=TrueLabel,startPoint=1)

QinglinMei/RCSL documentation built on Feb. 16, 2024, 2:31 a.m.